title: Identification of acute myocardial infarction from electronic healthcare records using different disease coding systems: a validation study in three European countries.
abstract: OBJECTIVE
  To evaluate positive predictive value (PPV) of different disease codes and free text in identifying acute myocardial infarction (AMI) from electronic healthcare records (EHRs).
  
  
  METHODS
  Validation study of cases of AMI identified from general practitioner records and hospital discharge diagnoses using free text and codes from the International Classification of Primary Care (ICPC), International Classification of Diseases 9th revision-clinical modification (ICD9-CM) and ICD-10th revision (ICD-10).
  
  
  METHODS
  Population-based databases comprising routinely collected data from primary care in Italy and the Netherlands and from secondary care in Denmark from 1996 to 2009.
  
  
  METHODS
  A total of 4 034 232 individuals with 22 428 883 person-years of follow-up contributed to the data, from which 42 774 potential AMI cases were identified. A random sample of 800 cases was subsequently obtained for validation.
  
  
  METHODS
  PPVs were calculated overall and for each code/free text. 'Best-case scenario' and 'worst-case scenario' PPVs were calculated, the latter taking into account non-retrievable/non-assessable cases. We further assessed the effects of AMI misclassification on estimates of risk during drug exposure.
  
  
  RESULTS
  Records of 748 cases (93.5% of sample) were retrieved. ICD-10 codes had a 'best-case scenario' PPV of 100% while ICD9-CM codes had a PPV of 96.6% (95% CI 93.2% to 99.9%). ICPC codes had a 'best-case scenario' PPV of 75% (95% CI 67.4% to 82.6%) and free text had PPV ranging from 20% to 60%. Corresponding PPVs in the 'worst-case scenario' all decreased. Use of codes with lower PPV generally resulted in small changes in AMI risk during drug exposure, but codes with higher PPV resulted in attenuation of risk for positive associations.
  
  
  CONCLUSIONS
  ICD9-CM and ICD-10 codes have good PPV in identifying AMI from EHRs; strategies are necessary to further optimise utility of ICPC codes and free-text search. Use of specific AMI disease codes in estimation of risk during drug exposure may lead to small but significant changes and at the expense of decreased precision.
authors.affiliation: Department of Medical Informatics, Erasmus MC University Medical Center, Rotterdam, The Netherlands.
authors.forename: Preciosa M
authors.initials: PM
authors.lastname: Coloma
authors.forename: Vera E
authors.initials: VE
authors.lastname: Valkhoff
authors.forename: Giampiero
authors.initials: G
authors.lastname: Mazzaglia
authors.forename: Malene Schou
authors.initials: MS
authors.lastname: Nielsson
authors.forename: Lars
authors.initials: L
authors.lastname: Pedersen
authors.forename: Mariam
authors.initials: M
authors.lastname: Molokhia
authors.forename: Mees
authors.initials: M
authors.lastname: Mosseveld
authors.forename: Paolo
authors.initials: P
authors.lastname: Morabito
authors.forename: Martijn J
authors.initials: MJ
authors.lastname: Schuemie
authors.forename: Johan
authors.initials: J
authors.lastname: van der Lei
authors.forename: Miriam
authors.initials: M
authors.lastname: Sturkenboom
authors.forename: Gianluca
authors.initials: G
authors.lastname: Trifirò
keywords: Epidemiology; Statistics & Research Methods